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K-Means clustering partitions your samples into k groups based on spectral similarity. You pick k, the algorithm assigns each sample to one of the k clusters. K-Means is useful for:
  • Discovering unknown classes in your data (e.g., variety, origin, defect type)
  • Validating that an expected categorization is actually visible in spectra
  • Generating cluster labels that you can promote to a categorical property

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Configuring a K-Means run

In the Configure run card on the analysis detail page, select K-Means.
K-Means configuration showing cluster count slider and reduction method toggle

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Parameters

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Choosing k

Start with k = 2 or 3. Higher values of k almost always reduce within-cluster variance (lower inertia) but the clusters become harder to interpret. The silhouette score helps you find the sweet spot.

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Reduction

By default, K-Means runs on the full preprocessed spectra. You can optionally reduce dimensions first: When you select PCA, an additional PCA components slider appears (default 5). When you select t-SNE, a Perplexity slider appears (default 30).
Clustering on a t-SNE embedding can create misleading clusters. t-SNE distorts global distances so the resulting clusters may not reflect real spectral similarity. Use NONE or PCA reduction unless you have a specific reason.
Click Launch run.

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Reading the results

K-Means run detail showing metrics table and cluster size bar chart

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Metrics table

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Silhouette interpretation

Run K-Means with several values of k and pick the one with the highest silhouette. This is the most reliable way to find the natural number of clusters in your data.

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Cluster sizes

Horizontal bar chart showing how many samples fell into each cluster. Each cluster gets a colour from the palette.

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What to do with cluster labels

Once K-Means finds clusters that match what you expected (e.g., the silhouette is high and the cluster sizes are reasonable), you can:
  1. Validate: open each cluster and look at the original samples. Are they meaningfully similar?
  2. Promote: create a new categorical property called something like “K-Means group” with categories matching the cluster IDs, then assign each sample to its cluster
  3. Train a classifier: use the new property as the target in an Experiment to train a model that predicts cluster membership from spectra
This last step is useful when the clusters represent something physically meaningful that you didn’t have property data for originally.

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Limitations

  • K must be set in advance: K-Means doesn’t find the “right” number of clusters on its own
  • Spherical assumption: K-Means assumes clusters are roughly round. Long, curved structures get cut into pieces
  • Sensitive to scale: preprocessing matters a lot. SNV or autoscale before K-Means
  • Random initialization: results can vary slightly between runs even with the same k